使用R中的嵌套循环加快蒙特卡洛模拟

Question

I would like to speed up the below monte carlo simulation of a DEA estimate

A<-nrow(banks)
effm<-matrix(nrow=A, ncol=2)
m<-20
B<-100

pb <- txtProgressBar(min = 0,
                     max = A, style=3)
for(a in 1:A) {
  x1<-x[-a,]
  y1<-y[-a,]
  theta=matrix(nrow=B,ncol=1) 

  for(i in 1:B){

    xrefm<-x1[sample(1:nrow(x1),m,replace=TRUE),]
    yrefm<-y1[sample(1:nrow(y1),m,replace=TRUE),]
    theta[i,]<-dea(matrix(x[a,],ncol=3),
                   matrix(y[a,],ncol=3),
                   RTS='vrs',ORIENTATION='graph',
                   xrefm,yrefm,FAST=TRUE)
  }

  effm[a,1]=mean(theta)
  effm[a,2]=apply(theta,2,sd)/sqrt(B)
  setTxtProgressBar(pb, a) 
}
close(pb)
effm 

Once A becomes large the simulation freezes. i am aware from online research that the apply function rapidly speeds up such code but am not sure how to use it in the above procedure.

Any help/direction would be much appreciated

Barry

Answer 1

The following should be faster.... but if you're locking up when A is large that might be a memory issue and the following is more memory intensive. More information, like what banks is, what x is, y , where you get dea from, and what the purpose is would be helpful.

Essentially all I've done is try to move as much as I can out of the inner loop. The shorter that is, the better off you'll be.

A <- nrow(banks)
effm <- matrix(nrow = A, ncol = 2)
m <- 20
B <- 100
pb <- txtProgressBar(min = 0,
                     max = A, style=3)
for(a in 1:A) {
  x1 <- x[-a,]
  y1 <- y[-a,]
  theta <- numeric(B)
  xrefm <- x1[sample(1:nrow(x1), m * B, replace=TRUE),] # get all of your samples at once
  yrefm <- y1[sample(1:nrow(y1), m * B, replace=TRUE),]
  deaX <- matrix(x[a,], ncol=3)
  deaY <- matrix(y[a,], ncol=3)

  for(i in 1:B){
    theta[i] <- dea(deaX, deaY, RTS = 'vrs', ORIENTATION = 'graph',
                   xrefm[(1:m) + (i-1) * m,], yrefm[(1:m) + (i-1) * m,], FAST=TRUE)
  }

  effm[a,1] <- mean(theta)
  effm[a,2] <- sd(theta) / sqrt(B)
  setTxtProgressBar(pb, a) 
}
close(pb)
effm